Bag-of-visual-words and spatial extensions for land-use classification

We investigate bag-of-visual-words (BOVW) approaches to land-use classification in high-resolution overhead imagery. We consider a standard non-spatial representation in which the frequencies but not the locations of quantized image features are used to discriminate between classes analogous to how words are used for text document classification without regard to their order of occurrence. We also consider two spatial extensions, the established spatial pyramid match kernel which considers the absolute spatial arrangement of the image features, as well as a novel method which we term the spatial co-occurrence kernel that considers the relative arrangement. These extensions are motivated by the importance of spatial structure in geographic data.

The methods are evaluated using a large ground truth image dataset of 21 land-use classes. In addition to comparisons with standard approaches, we perform extensive evaluation of different configurations such as the size of the visual dictionaries used to derive the BOVW representations and the scale at which the spatial relationships are considered.

We show that even though BOVW approaches do not necessarily perform better than the best standard approaches overall, they represent a robust alternative that is more effective for certain land-use classes. We also show that extending the BOVW approach with our proposed spatial co-occurrence kernel consistently improves performance.